Detecting annotation noise in automatically labelled data

Ines Rehbein, Josef Ruppenhofer


Abstract
We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.
Anthology ID:
P17-1107
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1160–1170
Language:
URL:
https://aclanthology.org/P17-1107
DOI:
10.18653/v1/P17-1107
Bibkey:
Cite (ACL):
Ines Rehbein and Josef Ruppenhofer. 2017. Detecting annotation noise in automatically labelled data. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1160–1170, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Detecting annotation noise in automatically labelled data (Rehbein & Ruppenhofer, ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1107.pdf
Data
English Web Treebank